System and method for field variance determination

ABSTRACT

A method for measuring performance of a geographic region from an image including a set of image elements includes: receiving the image corresponding to a time unit, generating a geographic region performance map for the image, combining the geographic region performance map with a second geographic region performance map, and generating a geographic region performance summary map. Generating the geographic region performance map for the image can include mapping a set of image elements to a set of geographic sub-regions, generating a set of vegetative performance values for the set of image elements, mapping the set of image elements to a set of crop types, defining a subset of image elements corresponding to a subset of vegetative performance values, comparing vegetative performance values of the subset of vegetative performance values, and generating geographic region performance values for the subset of image elements.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/012,762, filed 1 Feb. 2016, which claims the benefit of U.S.Provisional Application Ser. No. 62/109,888, filed on 30 Jan. 2015, andU.S. Provisional Application Ser. No. 62/130,314, filed on 9 Mar. 2015,which are all incorporated herein in their entireties by this reference.

This application is related to U.S. application Ser. No. 15/012,738filed 10 Feb. 2016 and titled “SYSTEM AND METHOD FOR CROP HEATHMONITORING”, and to those disclosed in related U.S. application Ser. No.15/012,749 filed 1 Feb. 2016 and titled “GROWTH STAGE DETERMINATIONSYSTEM AND METHOD”, which are herein incorporated in their entirety bythis reference.

TECHNICAL FIELD

This invention relates generally to the precision agriculture field, andmore specifically to a new and useful system and method for determiningperformance baselines for geographic regions in the precisionagriculture field.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic representation of a variation of the method.

FIGS. 2A to 2C are schematic representations of a specific example of amethod for measuring performance of a geographic region.

FIG. 3 is a schematic representation of a specific example of parts ofthe method as applied to a data set.

FIG. 4 is a schematic representation of a variation of the method.

FIG. 5 is a schematic representation of a variation of the method.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionis not intended to limit the invention to these preferred embodiments,but rather to enable any person skilled in the art to make and use thisinvention.

As shown in FIGS. 1-5, the method 100 for measuring performance of ageographic region from an image including a set of image elementsincludes receiving the image corresponding to a time unit S110,generating a geographic region performance map for the image S120, andgenerating a geographic region performance summary map S160 by combiningthe geographic region performance map with a second geographic regionperformance map. Generating the geographic region performance map forthe image S120 can additionally or alternatively include mapping imageelements to geographic sub-regions S125, generating vegetativeperformance values for the image elements S130, associating crop typeswith the image elements or geographic sub-regions S135, defining asubset of image elements S140, comparing vegetative performance valuesassociated with the subset of image elements S145, and/or generatinggeographic region performance values for the subset of image elementsS150.

The method 100 can additionally or alternatively include generating acrop input prescription S180 and/or notifying a user S190. The method100 can additionally or alternatively be repeated for each of aplurality of images of the same geographic region, each image recordedat different time units within a time duration (e.g., recorded atdifferent time units within a growing season or year). The method 100can additionally or alternatively be repeated for each of a plurality ofimages of the same geographic region, each image recorded atsubstantially the same time unit across different time durations (e.g.,recorded at substantially the same month or day within a growing seasonor year). These images can be aggregated and processed to generatesummaries of the geographic region variability for the time units, thetime durations, and/or any other suitable time period. The method can beperformed in real time, near-real time, asynchronously with imagegeneration, or at any other suitable frequency.

The method 100 functions to generate a performance map for thegeographic region (e.g., field) during the time duration, and canadditionally function to generate a performance summary map for thegeographic region over multiple time units, a time duration, and/ormultiple time durations. The performance summary map (e.g., yieldperformance map, yield proxy map) preferably provides an expected fieldperformance for a given recurrent time duration (e.g., recurrent month,time interval during the growing season, GDD, etc.), but canalternatively be used in any other suitable manner. In some variants,this method can function to identify high- or low-performanceagricultural fields or portions thereof.

1. BENEFITS

The method 100 can confer several benefits over conventional methods formeasuring performance. First, the method 100 uses remote monitoring data(e.g., satellite images), such that entire crop fields can be analyzedfor performance (e.g., yield performance, soil performance, geographicregion performance, etc.). This is in contrast with conventionalmethods, in which only small portions of the crop fields can be sampledfor measuring performance.

Second, the method 100 can measure crop field performance in real- ornear-real time (e.g., as the remote monitoring data is received). Thisis in contrast with conventional methods, which can only measureperformance after the growing season is over (e.g., by using remotelysensed or otherwise determined data collected over the entirety of thegrowing season) or when a user affirmatively requests a performanceanalysis (e.g., when a user physically enters the crop field to check oncrop health).

Third, because the method 100 is remotely monitoring the entiregeographic region over time, the method 100 can confer the additionalbenefit of recording and identifying changes in crop field performanceover a growing season, multiple growing seasons, and/or any suitableduration of time. These historical geographic region performance metricscaptured over time can be leveraged in generating an expected change inperformance over time, which can be compared against current changes inperformance for identifying crop health anomalies. Identifying crophealth anomalies over time can introduce the benefit of recording andidentifying anomaly patterns (e.g., geographic coverage pattern, spreadpattern, spread rate, etc.). These patterns can subsequently be used toidentify the cause of the anomaly, be used to recommend remedialtreatment, and/or be used in any other suitable manner.

Fourth, the method 100 can enable a user (e.g., a farmer) to viewprecision-level maps without providing their own information, developprecision-level prescriptions (e.g., treatments, crops, timing, etc.) toaccommodate for differences in field performance, detect crop anomalies,enable growth stage prediction of crops in each field segment, allowgrowers to manage their fields to optimal potential, and/or enable anyother suitable field performance-related functionality. Further, themethod 100 can enable the generation of geographic region performancemaps with superior image clarity and performance metrics compared totraditional yield maps and soil zone maps.

Fifth, by leveraging remote monitoring data for the entire crop field,the method 100 can normalize performance metrics for a geographic regionbased on crop types associated with the geographic region. For example,corn fields (e.g., pixels of growing corn) can be compared with cornfields, instead of comparing corn fields to wheat fields. The method 100can avoid normalizing the parameter value of a first crop (e.g., corn)with a parameter value derived from a second crop (e.g., wheat), whichcan mask the variation within the population of the first crop. As such,a crop-agnostic performance summary map of the underlying geographicregion can be generated based on historical performance values for thefield, irrespective of whether the field is currently growing multiplecrop types or a single crop type. Because this performance map isgenerated from the output of the geographic region (e.g., based on theplant performance), the performance map can accommodate for the effectsof soil, terrain, sun exposure, weather, groundwater, or any othersuitable factor that influences plant growth.

Sixth, the performance map can describe the relative performance of theunderlying geographic sub-regions over multiple growing seasons ratherthan over a single growing season. Such a map can account forvariability that might arise when relative performance is only measuredwithin a single growing season (e.g., variability in treatment types orpractices, imperfect treatments, pest infection, etc.). Further, bygenerating a performance map capturing relative performance over aminimum of a predetermined number of years (e.g., five years), themethod 100 can account for performance value outliers caused by pests,imperfect treatment application, and/or other anomalies.

2. METHOD 2.1 Receiving an Image.

As shown in FIGS. 2A-2C, 4, and 5, receiving an image S110 functions toobtain an image indicative of the performance of a geographic region.Receiving an image S110 can additionally or alternatively includeprocessing the image S111 and/or generating an image quality metric forthe image S115.

The image is preferably of a geographic region, more preferably of cropsin-situ within the geographic region. Alternatively, the image caninclude a portion of the geographic region (and the plants within thegeographic region) and/or areas surrounding the geographic region.However, the image can include any suitable content to be used inmeasuring the performance of the geographic region. The geographicregion can be an agricultural field (e.g., crop field), portion of anagricultural field, multiple contiguous agricultural fields, multipleseparate agricultural fields, a region encompassing both agriculturalfields and developed land, or be any other suitable geographic region.

The geographic region is preferably a two- or three-dimensional physicalregion, but can alternatively be one dimensional or be a point (e.g., ageographic location). The geographic region can be predetermined (e.g.,by a political entity or a user), dynamically determined (e.g.,automatically determined, etc.), or otherwise determined. The geographicregion can be defined by a geofence, political boundary, managementzone, common land unit, geological features (e.g., mountains, rivers,etc.), buildings, or defined in any other suitable manner. Thegeographic region can be identified by a geographic coordinate system(e.g., geographic latitude and longitude, UTM and/or UPS system,Cartesian coordinates, etc.), an address, a venue name, a common landunit identifier, a management zone identifier, or by any other suitableunique or non-unique identifier. The geographic region preferablyincludes geographic sub-regions that constitute the geographic region.However, any other suitable type of sub-unit can be used in constructingthe geographic region. The geographic region can be virtuallyrepresented within the system by a virtual model (e.g., a virtual map),an array of values (e.g., a value for each geographic sub-region withinthe geographic region), an identifier (e.g., a location identifier, suchas a set of coordinates, a geographic area, a venue name, etc.), or beotherwise virtually represented. A geographic sub-region can bevirtually represented within the system by a virtual model (e.g., asubset of the geographic region model), an identifier (e.g., ageographic identifier, a hash value, etc.), or be otherwise virtuallyrepresented. Each geographic sub-region virtual representation ispreferably associated with a physical geographic sub-region, and canadditionally be associated with image elements representing or capturingthe respective physical geographic sub-region. Each geographicsub-region virtual representation can be stored in association with oneor more sets of: spectral signals extracted from the associated imageelements, vegetative performance values calculated from the spectralsignals, relative performance metrics calculated from the vegetativeperformance values (e.g., normalized vegetative performance values),crop types (e.g., one for each growing season), or any other suitableinformation.

The image can be a two-dimensional image, a one-dimensional image, athree-dimensional image (e.g., generated from two or more images), orhave any suitable number of dimensions. The image can be a single imageor frame, as captured by the imaging system, or can be a composite image(e.g., mosaic) including multiple images that are stitched together. Invariations where the image is a composite image, the individual imagesconstituting the composite image are preferably recorded atsubstantially the same time unit. Alternatively, the individual imagesare recorded at different time units. However, the images making up thecomposite image can be recorded at any suitable time unit or time unitsover any suitable time duration or time durations. The image can be astill image, a kinetic image (e.g., a video), or have any other suitablekinetic parameter. The image can be a multispectral image, hyperspectralimage, ultraspectral image, be an image captured within the visiblerange, LIDAR-derived image, ultrasound-derived image, radar-derivedmeasurement, or be an image captured any other suitable electromagneticor acoustic frequency. Alternatively or additionally, a secondarymeasurement, such as electric conductivity (e.g., soil conductivity orEC), can be recorded by a secondary sensor and used as an image. In aspecific example, soil conductivity measurement values recorded over thegeographic region (e.g., with a ground-based soil conductivity meter)can be mapped to a virtual representation of the geographic region,correlated with other images of the geographic region, and used in themethod. However, any other suitable signal can be emitted and/orrecorded to generate the image. The image can be captured and/orreceived by an aerial system (e.g., satellite system, drone system,etc.), a terrestrial system (e.g., a camera dragged along the field by atractor), or any other suitable imaging system.

The image is preferably associated with one or more temporal indicators.The temporal indicator can be a time unit relative to a time duration,an absolute time (e.g., indicated by a global timestamp), or any othersuitable measure of time. The time duration can be a unique ornon-unique time duration. Examples of time durations include a uniqueyear (e.g., 2015), a unique growing season (e.g., spring of 2014, fallof 2009, etc.), a growth stage (e.g., vegetative stage, reproductivestage), relative time duration (e.g., spring, growth duration), or anyother suitable time duration. The time unit relative to the timeduration can be a time unit within the time duration (e.g., an hour of aday, a day of the week, a day of a month, a day of a year, a week of ayear, a month of a year, day of the planting season, growth stage of thegrowth duration, etc.), or be any other suitable time unit. The timeunit can be a recurrent time unit that recurs across multiple timedurations (e.g., January of 2015 and January of 2016, April 5 of Spring2015 and April 5 of Spring 2016, etc.).

The image is preferably received at a remote server that stores andprocesses the image. Alternatively, the image can be received at a userdevice, but can additionally or alternatively be received at anysuitable component. The image is preferably received from a third-partysource (e.g., via a third party service that captured the images), butcan be received from a direct source (e.g., directly from animage-taking component, directly from a user device of a grower whocaptured the image, etc.). However, the image can be received from anysuitable entity with any suitable relationship with the component (e.g.,remote server) receiving the image.

The image preferably includes a set of image elements. Types of imageelements can include a pixel, a superpixel, a digital value, an imagesegment, or any other suitable image element. Alternatively, the imagecan be a single image element. However, the image can include any numberof image elements defined in any suitable fashion.

In one variation, the image can be a satellite image encompassing one ormore agricultural fields. The image can be associated with a timestamp.The timestamp can be a relative timestamp, such as a time unit within atime duration (e.g., 5 th measurement of the year, a measurement inSpring, etc.), a global timestamp, such as a unique time (e.g., 2:34 pon May 3, 2012), or be any other suitable timestamp. In one example, thepixel-to-real-world distance (e.g., pixel to real-world meter, pixel toreal-world inch, etc.) can be known or estimated based on the satelliteheight from the field and focal length of the satellite camera. In thisexample, the image can be divided into a 5 meter by 5 meter grid, wherethe parts of the method 100 can be performed with respect to the grid.

2.1.A Pre-Processing an Image.

Receiving an image S110 can include pre-processing an image S111, whichfunctions to condition the image for generating a geographic regionperformance map based on the conditioned image. Processing the imagepreferably includes spectral band adjusting, where bands of the imageare adjusted and registered to other bands of the image. Additionally oralternatively, image processing can include compensating for variationin shadows resulting from topography variance and/or aerial imagerycaptured at different times of day. However, image processing canadditionally or alternatively include other types of image processingincluding: image sharpening, image smoothing, photo manipulation,brightness adjustments, and/or any other suitable type of imageprocessing. The image can be processed at a remote server, but canalternatively be processed at a user device and/or any other suitableentity.

In one variation, pre-processing an image includes processing the imagewith respect to other images of an image set. For example, each image ofthe set can be calibrated with respect to the remaining images of theset in order to enable accurate comparison of fields irrespective ofvariance in the time of day at which the images were captured.

2.1.B Generating an Image Quality Metric.

As shown in FIG. 5, receiving an image S110 can include generating animage quality metric for the image S115, which functions to calculate ametric measuring a suitability of the image for use in evaluatingperformance of a geographic region. The image quality metric can be usedin determining whether to include or exclude the image from a pool ofviable images to be used in evaluating geographic region performance.For example, if the image quality metric is below a specified imagequality metric threshold, the image can be excluded from furtherprocessing and analysis, such that the image will not be included inevaluating the performance of the geographic region. If the imagequality metric exceeds a threshold image quality metric, a geographicregion performance map can be generated for the image. Additionally oralternatively, the image quality metric can be used in combininggeographic region performance maps S160, such as by giving a lowerweight to a geographic region performance map generated from an imagewith a lower image quality metric. However, the image quality metric canbe additionally or alternatively used in other portions of the method100 including at least S120 and S160, and/or can be used in any othersuitable process. An image quality metric can be generated for eachimage of a set of images. Additionally or alternatively, an imagequality metric can be generated for the set of images (e.g., a singlemetric describing the quality of the set). However, image qualitymetrics can be generated for any number or combination of images. Animage quality metric can be generated for an image after receiving theimage S110 and prior to generating a geographic region performance mapfor the image S120. However, the image quality metric can be generatedat any time prior, in conjunction with, or after any suitable portion ofthe method 100.

Generating the image quality metric can include generating the imagequality metric based on an amount of cloud coverage present in theimage. Cloud coverage and the image quality metric preferably have anegative correlation, such that an increased amount of cloud coverageresults in a decreased image quality metric value. However, cloudcoverage and the image quality metric can have any suitablerelationship. In one variation, cloud coverage can be measured as apercentage of the image covered in clouds. For example, a probabilisticmodel can be employed for an image to generate masks for portions of theimage, where the masks indicate an obscured geographic sub-region (e.g.,obscured by clouds). The percentage of the image covered in a generatedmask can correspond to the percentage of the image covered in clouds. Ifthe cloud coverage percentage exceeds a specified cloud coveragethreshold, the image can be filtered out from further analysis.Alternatively, if the severity of cloud coverage exceeds a thresholdseverity for a given image segment, the image segment can be masked.However, exclusion of images and/or masking of image segments can bebased on any other suitable criteria. In another variation, cloudcoverage can be measured as a degree of cloud coverage covering thegeographic region at an image element (e.g., a high amount of cloudcoverage at a certain pixel). However, cloud coverage can be measuredwith any suitable unit of measurement or combination of units ofmeasurement.

Other criteria in determining an image quality metric can include: imageresolution, image blurriness, amount of the geographic region present inthe image, weather anomalies, user input (e.g., a user indication thatan image is unrepresentative of the geographic region), time unitcorresponding to the image, soil parameters, crop type, and/or any othersuitable criteria.

2.2 Generating a Geographic Region Performance Map.

As shown in FIGS. 2B and 3-5, generating a geographic region performancemap S120 functions to measure performance of geographic sub-regions ofthe geographic region at a time unit. Generating the geographic regionperformance map S120 can additionally or alternatively include mappingimage elements to geographic sub-regions S125, generating vegetativeperformance values S130, mapping image elements to crop types S135,defining a subset of image elements S140, comparing vegetativeperformance values S145, and generating geographic region performancevalues S150. The geographic region performance map is preferably avirtual model representing the crop performance of each of a set ofgeographic sub-regions (e.g., an array of values, etc.), but canalternatively be a virtual map representative of the crop performance ateach virtual position corresponding to each of a set of geographicsub-regions, or be any other suitable virtual representation of thegeographic region performance.

The geographic region performance map is preferably generated performedat a remote server, but can alternatively be performed at a user deviceand/or by any other suitable entity. Generating the geographic regionperformance map S120 can be entirely performed at a single component(e.g. at a remote server), but can alternatively be performed modularly,with portions of S120 performed at a first component (e.g., at a remoteserver), and other portions of S120 performed at a second component(e.g., at a user device). Images with an image quality metric exceedingan image quality metric threshold can each be used to generateindividual geographic region performance maps for the images.Alternatively, geographic region performance maps can be generated foreach image of a set of received images regardless of whether an imagequality metric has been generated for the image, and if an image qualitymetric was generated for the image, regardless of the image qualitymetric value. A single geographic region performance map can begenerated for an image. Alternatively, multiple geographic regionperformance maps (e.g., different maps generated by different means,different maps covering different areas of the geographic region,different maps indicating different performance metrics, etc.) can begenerated for a single image. However, a single geographic regionperformance map can be generated for multiple images, and any number orcombination of geographic region performance maps can be generated forany number or combination of images.

The geographic region performance map can be generated based onvegetative performance values (e.g., WDRVI values) corresponding toimage elements of the image. Additionally or alternatively, thegeographic region performance map can be generated based on supplementaldata including soil data (e.g., soil texture, soil hydraulic properties,soil organic matter, etc.), weather data (e.g., daily temperature,precipitation, radiation, etc.), and/or crop management data (e.g.,user-inputted data, historic seeding prescriptions, etc.). However, thegeographic region performance emap can be generated based on anysuitable data. The geographic region performance map can indicate theabsolute or relative crop performance (e.g., indicative of crop yield)at a given time unit for each geographic sub-region of the geographicregion, where the time unit is the time unit at which the image receivedin S110 was captured. Additionally or alternatively, the geographicregion performance map can indicate absolute or relative performancecharacteristics of soil, a crop, a crop input (e.g., seedingprescription, nitrogen prescription, etc.), and/or any other suitableentity.

Generated geographic region performance maps can be stored andthereafter used as a baseline for comparison with newly generatedgeographic region performance maps. Additionally or alternatively, thegeographic region performance maps can be used in enabling growth stageprediction of crops in field segments. The geographic region performancemaps can additionally or alternatively be used in any manner analogousto those disclosed in related U.S. application Ser. No.______ filed 1Feb. 2016 and titled “SYSTEM AND METHOD FOR CROP HEATH MONITORING”, andto those disclosed in related U.S. application Ser. No.______ filed 1Feb. 2016 and titled “GROWTH STAGE DETERMINATION SYSTEM AND METHOD”,which are herein incorporated in their entirety by this reference.

2.2.A Mapping Image elements to Geographic Sub-Regions.

As shown in FIGS. 2A and 4-5, generating a geographic region performancemap S120 can include mapping image elements to geographic sub-regionsS125, which functions to correlate image elements of the received imageto geographic sub-regions of the geographic region. Geographicsub-regions are preferably encompassed within the geographic region, butcan alternatively be separated from the geographic region or partiallyencompassed by the geographic region. However, image elements can bemapped to any combination of geographic sub-regions lying within,partially within, or outside the geographic region. The geographicsub-regions can possess characteristics of a type that the geographicregion can possess (e.g., can be an agricultural field, developed land,single or multi-dimensional, predetermined, dynamically determined,etc.). Alternatively, the geographic sub-regions can possesscharacteristics exclusive of those capable of being possessed by thegeographic region. However, the geographic sub-regions and thegeographic region can exhibit any suitable trait for defining a regionupon which performance can be assessed.

Image elements can be mapped to geographic sub-regions S125 based oncorrespondence of the image element with a geographic coordinate (e.g.,geographic latitude and longitude, UTM and/or UPS system, Cartesiancoordinates, etc.), an address, a venue name, a common land unitidentifier, a management zone identifier, or by any other suitableunique or non-unique identifier. Additionally or alternatively, mappingimage elements to geographic sub-regions S125 can be based on previouslyreceived images that have had their image elements mapped to geographicidentifiers that are present in the geographic region of the currentimage. Further, the mapping can additionally or alternatively beperformed based on user input associating sections of the received imageto geographic sub-regions. However, image elements can be mapped togeographic sub-regions in any suitable fashion.

Each image element of the set is preferably mapped to a geographicsub-region. Each image element is preferably mapped to a separate anddiscrete geographic sub-region from the remainder of the image elementset, but can alternatively be mapped to the same geographic sub-regionas another image element of the set, be mapped to a geographicsub-region overlapping with the geographic sub-region corresponding toanother image element of the set, or be mapped to any other suitablegeographic sub-region or set thereof. However, any number or combinationof image elements can be mapped to any number or combination ofgeographic sub-regions (e.g., in a 1:1 relationship, in a greater than 1to 1 relationship, in a 1 to greater than 1 relationship, etc.).

In a first variation, image elements are mapped to geographicsub-regions in a manner enabling the image elements to be characterizedin relation to other image elements based on their respectivecorrespondences with geographic sub-regions. In this variation, a firstimage element can be mapped to a first geographic sub-region, and asecond image element can be mapped to a second geographic sub-regionbased on: the relationship between the first and second image elementsin the image and the mapping between the first image element and thefirst geographic sub-region. For example, a first geographic sub-regioncorresponding to a first image element can be characterized as locatedimmediately west of a second geographic sub-region, enabling a secondimage element to be mapped to the second geographic sub-region based onthe second image element's relationship to the first image element.Geographic sub-regions and image elements of a given image arepreferably capable of being characterized in relation to geographicsub-regions and image elements of other images, irrespective of whetherthe geographic sub-regions of the other images are present in the givenimage. However, the mapping of image elements to geographic sub-regionsS125 can enable any suitable characterization of image elements and/orgeographic sub-regions in relation to any other image elements and/orgeographic sub-regions.

In a second variation, associating an image element with a geographicsub-region (or geographic location) can include: determining ageographic location associated with a reference point on the image(e.g., the upper right corner of the image, image center, etc.),determining a relationship between the image element and the referencepoint (e.g., three pixels to the right of the reference point),determining a physical geographic distance corresponding to arelationship unit (e.g., each pixel width represents a geographicdistance of 5 m), determining the geographic location represented by theimage element based on the relationship and physical geographic distancecorresponding to the relationship unit (e.g., the image element isassociated with the geographic sub-region having a location 15 m to theright of the geographic location associated with the reference point).The geographic location associated with the reference point on the imagecan be determined based on: the location of the image-capturing devicewhen the image was recorded, the timestamp of the image and theimage-capturing device trajectory, landmarks appearing within the image,or otherwise determined. The physical geographic distance correspondingto a relationship unit can be determined based on the field of view ofthe image-capturing device, the distortion of the image-capturingdevice, or be otherwise determined.

In a third variation, the image is received from the image source withall image elements pre-associated with a geographic location and/orgeographic sub-region identifier. However, image elements can beotherwise mapped to geographic sub-regions.

2.2.B Generating Vegetative Performance Values.

As shown in FIGS. 2A and 4-5, generating a geographic region performancemap S120 can include generating vegetative performance values S130,which functions to generate a measure of vegetative performance acrossthe geographic region captured by the image. The plant parameters canadditionally or alternatively be used to calibrate, generate, apply, orotherwise use a deterministic model (e.g., DSSAT, WOFOST, APSIM, etc.)to the geographic region. The plant parameters, more preferably thevegetative performance values, can be extracted before imagesegmentation by crop type, after image segmentation by crop type, or beextracted at any other suitable time. The vegetative performance valuespreferably indicate the crop performance at a given time unit for ageographic sub-region of the geographic region, where the time unit isthe time unit at which the image received in S110 was captured.Additionally or alternatively, the vegetative performance values canindicate performance characteristics of soil, a crop input (e.g.,seeding prescription, nitrogen prescription, etc.), and/or any othersuitable variable for the geographic sub-region. However, the vegetativeperformance value can measure any suitable characteristic of ageographic sub-region, geographic region, and/or any suitable area atany relevant time unit.

The plant parameter can be a physiological measurement, morphologicalmeasurement, or any other suitable parameter descriptive of one or moreplants. Physiological measurements can include vegetation indices (e.g.,vegetative performance values), chemical measurements, or any othersuitable physiological measurements. Vegetative indices can includeNormalized Difference Vegetation Index (NDVI), Wide dynamic rangevegetation index (WDRVI), Transformed Chlorophyll Absorption inReflectance Index normalized by Optimized Soil-Adjusted Vegetation Index(TCARI/OSAVI), Normalized Difference Red Edge Index (NDRE), CanopyChlorophyll Content Index (CCCI), Photochemical Reflectance Index (PRI),crop water stress index (CWSI), canopy temperature less air temperature(Tc−Ta), stomatal conductance (G), stem water potential, water stress,water content, Water Band Index (WBI), plant uniformity across thegeographic area, Leaf Area Index (LAI), Net Assimilation Rate (NAR),Relative Growth Rate (RGR), Leaf Area Ratio (LAR), Leaf Area Duration(LAD), Crop Growth Rate (CGR), vegetative performance value change overtime, vegetative performance value change rate, absolute growth rate involume, absolute growth rate in number, absolute growth rate in mass,plant density over the geographical region, and/or any other suitablevegetative or plant index or combination thereof.

Generating the vegetative performance values can include measuring thesignal from one or more spectral channels (e.g., visual signal,intensity of one or more wavelengths, etc.), processing the imagethrough image processing techniques (e.g., extracting points ofinterest, gradients of interest, or any other suitable image feature ofinterest), or otherwise extracting a parameter value from the image. Thevegetative performance value can be extracted for a pixel of the image,for a sub-region of the geographic region within the image field of view(e.g., where each pixel can be mapped to a predetermined geographic areabased on a known or estimated height of the imaging system and focallength of the imaging system), for the entire image, or for any suitableimage element.

A set of vegetative performance values can be generated for a set ofimage elements of the image received in S110, such that a vegetativeperformance value is generated for each image element of the set ofimage elements. Alternatively, vegetative performance values can begenerated only for a subset of the image elements of the image. However,any number or combination of vegetative performance values can begenerated for any number or combination of image elements (e.g., in a1:1 relationship, in a greater than 1 to 1 relationship, in a 1 togreater than 1 relationship, etc.). In the variation where vegetativeperformance values are generated for only a subset of the imageelements, selecting image elements for which to generate a vegetativeperformance value can be based upon image characteristics (e.g., imagequality at the image element), weather characteristics (e.g., cloudcoverage over a geographic sub-region), soil characteristics (e.g., soilhealth at a geographic sub-region), crop type (e.g., generatingvegetative performance values only for geographic sub-regions growingcorn as opposed to other crop types), crop inputs (e.g., generatingvegetative performance values only for the geographic regions undergoinga specified seeding regimen or prescription), and/or any other suitablecharacteristics.

2.2.C Mapping Image Elements to Crop Types.

As shown in FIGS. 2A and 4-5, generating a geographic region performancemap S120 can include mapping image elements to crop types S135, whichfunctions to segment the image by crop type, such that commodities canbe masked out. Additionally or alternatively, image elementscorresponding to cloud coverage, crop health anomalies, or otheraberrations can be masked out. The image can be segmented intosubstantially contiguous regions, each associated with a single croptype of a set of crop types. Alternatively or additionally, a region canbe associated with multiple crop types from the set of crop types,and/or different regions can be associated with different crop typesfrom the set of crop types. However, the image can be otherwisesegmented. This enables corn fields (e.g., pixels of growing corn) to becompared with corn fields, instead of comparing corn fields to wheatfields. This segmentation can be desirable because different crop typescan exhibit different vegetative performance values at a given growthstage or time unit. Because the image can encompass multiple crop typeswithin its field of view, normalizing the parameter value of a firstcrop (e.g., wheat) with a parameter value derived from a second crop(e.g., corn) can mask the variation within the population of the firstcrop. For example, for corn and wheat fields that are planted atsubstantially the same time, corn can exhibit a WRDVI frequency patternthat is strongly positively skewed with a mode of approximately −0.75 ata first time (e.g., May), while the wheat can exhibit a WRDVI frequencypattern that is substantially uniform with a mode of approximately −0.02at the first time. Normalizing the wheat WDRVI values with anormalization factor derived from corn WDVRI values would mask the WDRVIvariation within the wheat population. The segments preferably encompassa plurality of image elements (e.g., pixels, sub-regions, grid units,etc.), but can alternatively encompass a single image element or be aportion of the image element.

Each image element is preferably mapped to a crop type, classified asnot corresponding to a crop type, and/or classified as not correspondingto any crop type. Alternatively, some image elements of the receivedimage can forego classification into a crop type. However, any number orcombination of image elements can be mapped to any number or combinationof crop types (e.g., in a 1:1 relationship, in a greater than 1 to 1relationship, in a 1 to greater than 1 relationship, etc.). For eachimage element that is mapped to a geographic sub-region as in S125, avegetative performance value can be generated for that image element asin S130, and that image element is mapped to a crop type as in S135.However, for a given image element, any suitable combination of portionsof the method 100 can be performed for the image element and in anysuitable order.

In a first variation, mapping image elements to crop types can includeidentifying a geographic sub-region associated with a crop type;identifying an image element corresponding to the geographic sub-region;and associating the image element to the crop type. Identifying ageographic sub-region associated with a crop type can include:overlaying a predetermined map of crop types for the geographic regionover a virtual map of the geographic region; overlaying a predeterminedmap of crop types for the geographic region over the image based on theassociation between the image geographic location and a geographiclocation associated with the predetermined map; or otherwise identifyingthe type of crop currently growing on the geographic sub-regions of thegeographic region.

The predetermined map can be automatically retrieved from a remotereporting system, automatically generated based on the image, receivedfrom a user, automatically determined by a precision agriculture system,or be otherwise determined. In a first example, the map from the remotereporting system can be the cropland data layer corresponding to thetime duration. In a second example, the map from the remote reportingsystem can be a soil survey layer. In a third example, the map can beautomatically generated based on the image by matching the plantparameter pattern for the geographic sub-regions over time with a knownplant parameter pattern for the crop. In a fourth example, a crop typeuser input can be received, where the crop type user input associates acrop type to a geographic region or sub-region. The map can be a set ofmanagement regions or zones received from a farmer, where the farmer candefine fields and assign crops to each field. In a fifth example, theprecision agriculture system can generate the map on a per-plant orper-sub-region basis, where the system can automatically determine theposition and crop type for each plant as it passes by the plant. In asixth example, the system can automatically determine the crop type foreach geographic sub-region based on the crop growth pattern (e.g.,greening parameters over the course of the growing season). However, theimage can be segmented according to crop type in any other suitablefashion.

In a second variation, mapping image elements to crop types S135 caninclude automatically classifying the image elements with a crop type,based on the visual signal of the image itself (e.g., the intensity of aset of wavelengths). Classifying the image elements can includeprocessing the image and analyzing the image to classify an imageelement. Processing the image can include segmenting the image whereinthe image elements within each segment preferably share similarcharacteristics (e.g., similarities in color, quality, objectsrepresented by the image segments, image element characteristics, etc.),or otherwise processing the image. Analyzing the image can includeclassifying the image segments as corresponding to a specific crop type(e.g., wheat, corn, etc.), such that image elements contained by a givenimage segment will be mapped to the crop type or crop typescorresponding to the image segment. The image segment can be classifiedbased on characteristic values (feature values) of the image segment(e.g., shape, normalized percentile, etc.), characteristic values of theimage elements within the segment (e.g., number, normalized percentiledistribution, etc.), or be otherwise classified. The image segment canbe classified using a classification module (e.g., applyingclassification algorithms), regression module, or any other suitablemodule. However, the image can be processed and/or analyzed in anysuitable manner in mapping image elements to crop types S135.

2.2.D Defining a Subset of Image Elements.

As shown in FIGS. 2B and 4-5, generating a geographic region performancemap S120 can include defining a subset of image elements S140, whichfunctions to identify related image elements for comparison to generatethe crop- and/or treatment-agnostic metric. The defined subset of imageelements is preferably a subset of the set of image elementscorresponding to the image received in S110, but the subset of imageelements can include any suitable image element. The subset of imageelements is preferably defined after crop types have been assigned tothe image elements. The subset of image elements is preferably definedafter crop types have been assigned to the image elements, and after aset of vegetative performance values have been generated for the imageelements. However, S140 can be performed prior to or after any suitableportion of the method 100. However, any number and/or combination ofimage elements can be mapped or not mapped to any number and/orcombination of crop types, and can correspond to any number and/orcombination of vegetative performance values.

In a first variation, defining the subset of image elements is basedupon crop type. Every image element of the subset of image elements ispreferably mapped to a single crop type of the set of crop types. Forexample, each image element of the subset of image elements cancorrespond to the crop type of corn. Further, the subset of imageelements can constitute all image elements mapped to the given crop type(e.g., for an image, all of the image elements associated with the croptype of corn). The number of defined subsets of image elements for theimage will preferably match the number of crop types present in theimage (e.g., two defined subsets of image elements if the only croptypes present in the image are corn and wheat). Alternatively, thenumber of defined subsets of image elements can vary from the number ofcrop types present in the image, but any number of subsets of imageelements can be defined in relation to the number of crop types in theset of crop types. Additionally or alternatively, a single subset ofimage elements can be mapped to a combination of different crop types ofthe set of crop types (e.g., the subset image elements can constituteeach image element corresponding to corn and/or wheat, but not includeimage elements corresponding to other crop types), but can also bemapped to any number (e.g., o) and/or combination of crop types.

In a second variation, defining the subset of image elements is basedupon vegetative performance values. For example, the criteria forincluding an image element in the subset of image elements can bedependent on vegetative performance values exceeding a threshold value,being below a threshold value, being within a range of vegetativeperformance values, being a specific vegetative performance value,and/or any other criteria with respect to vegetative performance values.

In a third variation, defining the subset of image elements can be basedupon crop types and vegetative performance values. Defining a subset ofimage elements can be based upon the criteria of corresponding to aspecific crop type and vegetative performance value characteristic. Forexample, a subset of image elements can be defined as image elementsmapped to corn, and with a vegetative performance value exceeding avalue of −0.2. However, the subset of image elements can be selectedbased on any suitable number and/or combination of crop characteristics,vegetative performance characteristics, and/or other characteristics(e.g., soil characteristics, weather characteristics, crop inputcharacteristics, image characteristics, etc.).

2.2.E Comparing Vegetative Performance Values.

As shown in FIGS. 2B and 3-5, generating a geographic region performancemap S120 can additionally include comparing the vegetative performancevalue with a reference performance value S145, which functions to assessthe relative vegetative performance for the current growing season. Thiscan be performed prior to normalization, during normalization, or afternormalization. The reference performance value is preferably indicativeof expected performance for the geographic subregion, but canalternatively be indicative of a preferred performance for thegeographic subregion or be indicative of any other suitable performance.The reference performance value is preferably determined based onhistoric vegetative performance values for a geographic area (e.g.,vegetative performance values associated with times before the firsttime, times before the instantaneous time, times before theinstantaneous growing season, etc.), but can alternatively be determinedbased on new vegetative performance values (e.g., vegetative performancevalues associated with the first image), market predictions, a referencevalue received from a user, or be otherwise determined. The geographicarea can be the geographic sub-region, geographic region, adjacentgeographic region or any other suitable geographic location. Examples ofthe reference performance value include: a single historic vegetativeperformance value, an average, mode, and/or median vegetativeperformance value generated from a set of vegetative performance values,a predetermined value, a dynamically determined value, an automaticallydetermined value, a user-inputted value, or any other suitable value.However, the reference performance value can be any suitably determinedcomposite and/or individual vegetative performance value. Vegetativeperformance values of a single type (e.g., WDRVI) are preferablycompared to vegetative performance values of the same type, but canadditionally or alternatively be compared to vegetative performancevalues of different types.

In a first variation, comparing the vegetative performance value with arelative performance value includes ranking the vegetative performancevalues. In this variation, the relative performance value can be a priorrelative performance value recorded during the same growing season. Forexample, a relative ranking of vegetative performance values of thesubset of image elements can be determined, depending on the respectivevegetative performance value. Alternatively, ranking vegetativeperformance values can be based on absolute magnitude of vegetativeperformance values, variation in vegetative performance value, and/orany other characteristics of the vegetative performance values. However,vegetative performance values corresponding to the subset of imageelements can be compared in any suitable manner.

In a second variation, comparing the vegetative performance value with arelative performance value includes calculating the difference betweenthe vegetative performance value and the reference performance value. Inthis variation, the reference performance value can be a summary ofhistoric vegetative performance values for the geographic region (e.g.,an average of the historic vegetative performance values for theregion), a vegetative performance value for a different geographicsub-region captured in the same image (e.g., a geographic sub-regioncorresponding to the same crop type associated with the non-referencevegetative performance value), or any other suitable type of referenceperformance value. In one example, calculating the difference caninclude subtracting an average reference performance value (e.g.,averaged from historic vegetative performance values for the geographicregion) from the vegetative performance value. In a second example,calculating the difference can include dividing the difference by anaverage performance value (e.g., averaged from historic vegetativeperformance values associated with a recurring time unit from a previousgrowing season).

In a third variation, comparing the vegetative performance value with arelative performance value includes smoothing the vegetative performancevalue. In this variation, vegetative performance values can be smoothedbased on vegetative performance values associated with neighboring imageelements, non-neighboring image elements, image elements from otherimages, images, predetermined values, dynamically determined values,automatically determined values, and/or any other suitable performancevalues. Alternatively, vegetative performance values can be smoothedbased on other types of performance values (e.g., geographic regionperformance values). Smoothing techniques that can be performed include:linear filtering, adaptive filtering, Gaussian smoothing, movingaverage, Laplace smoothing, exponential smoothing, and/or any othersuitable type of smoothing. The set of vegetative performance values tobe smoothed can include any number and/or combination of vegetativeperformance values. In one example, Gaussian smoothing is performed withrespect to a vegetative performance value and the immediately adjacentvegetative performance values associated with a single image. In anotherexample, smoothing is performed with respect to vegetative performancevalues for the same geographic sub-region, each vegetative performancevalue associated with a different image captured at a different time.However, smoothing the vegetative performance value can otherwise beperformed.

2.2.F Generating Geographic Region Performance Values.

As shown in FIGS. 2B and 3-5, generating a geographic region performancemap S120 can include generating geographic region performance valuesS150, which functions to generate a crop- and/or treatment-agnosticmetric to measure land performance. Generating geographic regionperformance values S150 is preferably based upon comparing vegetativeperformance values as in S145, but can additionally or alternatively bebased upon any other suitable characteristic (e.g., soilcharacteristics, weather characteristics, crop type, crop input,user-defined criteria, etc.) or analysis of characteristics. Thegeographic region performance value can be numerical (e.g., 0.5, 78%,etc.), categorical (e.g., low performance, medium performance, highperformance), visual (e.g., red color for low performance, yellow colorfor medium performance, green color for high performance), and/orauditory, but can be of any suitable format or combination of formats.

The performance of the geographic sub-region can directly and/orindirectly indicate or correlate with performance of yield, soil, crop,crop input, and/or any other suitable parameter. However, the geographicregion performance value can additionally or alternatively indicateperformance of the entire geographic region, surrounding geographicregions, and/or any other suitable area at any given time unit or timeduration. Geographic region performance values are preferably calculatedfor each subset of image elements defined in S140, such that each imageelement of each subset of image elements corresponds to a geographicregion performance value, thereby constituting a geographic regionperformance map for the geographic region. Alternatively, geographicregion performance values are generated only for a defined selection ofimage elements of the subset of image elements. For example, geographicregion performance values can be calculated based on the comparison ofvegetative performance values in S145, where geographic regionperformance values will only be calculated for image elementscorresponding to vegetative performance values exceeding a thresholdvegetative performance value. However, any number and/or combination ofgeographic region performance values can be generated for any numberand/or combination of image elements present in the received imageand/or other images (e.g., in a 1:1 relationship of geographic regionperformance values to image elements, a greater than 1 to 1relationship, a 1 to greater than 1 relationship, etc.).

2.2.F.i Normalizing Parameter Values.

In a first variation, generating geographic region performance valuesS150 can include normalizing (e.g., through L1 normalization, L2normalization, etc.) the parameter values (e.g., vegetative performancevalues). This can additionally or alternatively function to rescale anyoutlying image element values to the same set range as the other imageelements of the population, without skewing the image element valuedistribution. Normalizing the parameter values can reduce or eliminatethe need for outlier identification and/or removal. Normalized parametervalues can be further processed to generate geographic regionperformance values. For example, normalized parameter values can bemapped to geographic region performance values possessing a differentformat for display to a user (e.g., visual geographic region performancevalues of red, yellow, and green geographic areas corresponding to“low,” “medium,” and “high” performance). Alternatively, the geographicregion performance values can be the normalized parameter values. In oneexample, normalizing compares corn image elements (e.g., pixels orregions) to corn image elements, while wheat image elements are comparedto wheat image elements, even though the wheat and corn image elementsare encompassed within the same image.

The parameter values can be normalized: per image element subset, perimage segment, per image pixel (e.g., where pixels can be sub-componentsof the image segment, or each image segment can include a set ofpixels), per geographic sub-region (e.g., grid unit), or per any othersuitable set of image elements. The population of image elements used tonormalize the parameter value for a first image element (normalizingpopulation) can be the population of image elements associated with thesame crop type within the same image as the first image element, thepopulation of image elements associated with the same crop type within asecond image, the population of image elements associated with the samecrop type within a composite image, or be any other suitable populationof image elements.

In a first example, the normalizing population can be the population ofpixels or other suitable image element type associated with the samecrop type within the same image segment (e.g. a set of image elements)as the pixel to be normalized (first image segment). In a secondexample, the normalizing population can be the population of pixelswithin a set of secondary image segments associated with the same croptype as the pixel to be normalized, where the set of secondary imagesegments can be separate and distinct image segments within the sameimage as the first image segment. In a third example, the normalizingpopulation can be the population of pixels within a set of secondaryimage segments associated with the same crop type as the pixel to benormalized, where the set of secondary image segments can be separateand distinct image segments identified within a set of secondary images.Each secondary image segment can represent the same geographicsub-region (e.g., where the first and second images overlap) as that ofthe first image segment, or represent a different geographic sub-region.Each secondary image segment can be within a predetermined pixeldistance from the pixel to be normalized, represent a second geographicsub-region within a predetermined geographic distance from the firstgeographic sub-region represented by the first image segment, be animage segment representing a geographic sub-region associated with acommon user (e.g., farmer) as the geographic sub-region represented bythe first image segment, be an image segment representing a geographicsub-region associated with a different user (e.g., farmer) from thegeographic sub-region represented by the second image segment, or be anyother suitable second image segment.

In a first specific example, the WDRVI value for a first pixelassociated with corn is normalized based on the WDRVI values forremaining pixels from the subset of image elements defined in S140(e.g., image elements from the same image segment, all corresponding tocorn). In a second specific example, the NDVI value for a first pixelassociated with corn is normalized based on the NDVI values forsecondary pixels from the same image that are also associated with corn.In a third specific example, the WDRVI value for the first pixelassociated with corn is normalized based on the WDRVI values forsecondary pixels from a second image, captured at substantially the sametime and representing a different geographic region from thatrepresented by the image containing the first pixel. The differentgeographic region can be part of the same field as the first geographicregion, or can be part of a different field. In a fourth specificexample, the WDRVI value for a first grid unit associated with corn isnormalized based on WDRVI values for grid units cooperatively formingthe corn field with the first grid unit. In a fifth specific example,the NDVI value for a first grid unit associated with corn is normalizedbased on WDRVI values for other corn fields recorded at substantiallythe same time as the image used to determine the NDVI value for thefirst grid unit. The other corn fields can be owned by the same farmer,owned by different farmers, located within a threshold geographicdistance of the first corn field, or be any other suitable corn field.

Normalizing the image element values can include determining apercentile ranking of a vegetative performance value relative toremaining vegetative performance values of the subset of vegetativeperformance values, but the image element value can be otherwisenormalized. In this variation, the percentile ranking can be determinedbased on the ranking of vegetative performance values from comparing thevegetative performance value to a reference vegetative performance valueS145. However, normalization can additionally or alternatively be basedupon any other suitable type of ranking or comparison of vegetativeperformance values in relation to one another. The percentile ispreferably determined relative to the normalizing population, but can beotherwise determined. In one example, the image element can be a firstpixel of a first image, while the normalizing pixel population is theremainder of the subset to which the image element belongs. Normalizingthe parameter value for the first pixel can include determining whichpercentile the first pixel's parameter value falls into, relative to theparameter values of the normalizing pixel population. Alternatively,normalizing the image element values can include determining theprobability of each image element's parameter value, given the parametervalue distribution of the normalizing population. The probability cansubsequently be used as the normalized value, or be used in any othersuitable manner. However, the image element values can be otherwisenormalized.

2.2.F.ii Identifying and/or Removing Outliers.

In a second variation, generating geographic region performance valuesS150 can include identifying and/or removing outliers. In one example,outliers can be identified within the normalizing population. Outlierscan result from mislabeled geographic sub-regions (e.g., where a portionof a corn field is accidentally recorded as growing wheat), mistreatmentof the geographic sub-region (e.g., where a portion of the corn field isaccidentally over-fertilized), or result in any other suitable manner.Outliers can be identified as image elements having values fallingwithin a predetermined percentile (e.g., within the 20th percentile),values falling outside a predetermined percentile (e.g., above the 90thpercentile, etc.), values falling outside a predetermined percentilerange, values below a threshold probability of occurrence (e.g., below40% probability of occurrence), normalized values below a thresholdprobability of occurrence given the normalized values of the pixel'sneighbors, or be identified in any other suitable manner.

Subsequent to identification of outliers, the outliers can be removedfrom the normalizing population, such that the respective parametervalues do not affect (e.g., skew) the normalization. Outlier removal cancontinue until a threshold variance within the normalizing population isachieved, until a minimum number of image elements has been reached, oruntil any other suitable cessation event occurs. The outlying imageelement can additionally or alternatively be flagged and removed fromsubsequent iterations of the method (e.g., for a second time duration).The crop corresponding to the outlying image element can additionally oralternatively be determined (e.g., based on parameter comparison withadjacent image elements, received from a user, etc.) and stored inassociation with the image element. The respective parameter value forthe previously outlying image element can additionally or alternativelybe included in the normalizing population for the newly determined cropcategorization. However, outliers can be otherwise determined andprocessed.

2.2.F.iii Generating a Geographic Region Performance Map.

In a third variation, generating geographic region performance valuesS150 can include associating the geographic region performance valueswith the geographic location corresponding to each image element,thereby providing a map of geographic sub-regions and/or regionsassociated with metrics of relative performance. The resolution of theresultant map is preferably based on the resolution of the imageelement, but can alternatively be higher or lower. The resolution can besubstantially constant across the map or be variable. In one example,the resolution of the resultant map can be the real-world geographicarea represented by a pixel of the image. In another example, theresolution of the resultant map can be based on the image segments.However, the resolution can be otherwise determined. In one variation ofthe method in which the image element is an image pixel, associating thegeographic region performance values with geographic locations includesdetermining a set of real-world geographic locations represented by thepixel (e.g., a geographic region, a geographic identifier for thegeographic region, a geographic location within the geographic region,etc.) and assigning the geographic region performance value (e.g., valuepercentile, value probability, etc.) to the set of real-world geographiclocations within a database or other storage system. However, thegeographic region performance values can be otherwise associated withgeographic locations and/or regions.

2.3 Generating a Geographic Region Performance Summary Map.

As shown in FIGS. 2C-5, generating a geographic region performancesummary map S160 functions to generate a virtual model indicative of theexpected performance for each geographic sub-region of the geographicregion during one or more recurring time units. The geographic regionperformance summary map is preferably generated based on the geographicregion performance maps for a recurring time unit across multiple timedurations (e.g., all performance maps for August, across multipleyears), but can alternatively be generated based on the geographicregion performance maps for all time units within a time duration (e.g.,all performance maps for all months within a year), or be generatedbased on any other suitable set of performance maps. A single geographicregion performance summary map is preferably generated for a givengeographic region, but any suitable number of performance maps can begenerated for the geographic region. For example, different summary mapscan be generated to measure different performance metrics (e.g., a maptailored to measure performance of a specific seeding prescription, amap tailored to measure yield performance generally, etc.). However, anynumber or type of geographic region performance summary maps can begenerated for any number of geographic regions. A generated geographicregion performance summary map can be updated with a new geographicregion performance map. For example, the method 100 can includereceiving a new image corresponding to the geographic region, generatinga new geographic region performance map for the new image, andgenerating an updated geographic region performance summary map bycombining the geographic region performance summary map with the newgeographic region performance map.

Generated geographic region performance summary maps can be stored andthereafter used as a baseline for comparison with newly generatedgeographic region performance maps or geographic region performancesummary maps. Additionally or alternatively, the geographic regionperformance summary maps can be used in enabling growth stage predictionof crops in field segments. The geographic region performance summarymaps can additionally or alternatively be used in any manner analogousto those disclosed in U.S. application Ser. No.______ filed 1 Feb. 2016and titled “SYSTEM AND METHOD FOR CROP HEATH MONITORING”, and to thosedisclosed in related U.S. application Ser. No. filed 1 Feb. 2016 andtitled “GROWTH STAGE DETERMINATION SYSTEM AND METHOD”, which are hereinincorporated in their entirety by this reference.

In a first variation, generating the geographic region performancesummary map S160 includes combining a set of geographic regionperformance maps (e.g., maps with normalized performance values for eachof a set of geographic sub-regions), but can be otherwise generated.Combining the set of geographic region performance maps can includecombining a current geographic region performance map with at least onehistoric geographic region performance map for the same geographicregion. However, any number of geographic region performance maps can becombined. Alternatively, combining geographic region performance mapscan be omitted.

Combining geographic region performance maps S160 preferably includescombining individual geographic region performance maps for thegeographic region corresponding to related time units (e.g., acrossmultiple time durations). The time unit can be days, weeks, months,years, growing season, or any other suitable time unit. In a firstexample, geographic performance maps for August can be combined acrossmultiple years. In a second example, geographic performance maps for thefirst week of July can be combined across multiple years. In a thirdexample, geographic performance maps for all months within a year can becombined. However, the geographic performance maps can be otherwisecombined. The geographic region performance maps can be combined can beon a per-image element basis (e.g., per-pixel, per-image segment,per-image, etc.), per geographic area basis (e.g., per-geographicsub-region, per geographic region, etc.), or any other suitable basis.Performance values for the same geographic sub-region are preferablycombined, but performance values for different geographic sub-regionscan alternatively be combined. However, selection of geographic regionperformance values to combine can be based on any suitablecharacteristics (e.g., crop type, soil characteristics, weathercharacteristics, geographic region performance values, vegetativeperformance values, etc.) or criteria. Performance values of thegeographic region performance maps can be smoothed before or aftercombining maps. Smoothing techniques that can be performed include:linear filtering, adaptive filtering, Gaussian smoothing, movingaverage, Laplace smoothing, exponential smoothing, and/or any othersuitable type of smoothing.

In a first variation, a current geographic region performance map iscombined with a historic geographic region performance map. In a firstexample, the first geographic region performance map corresponds to afirst instance of a first recurrent time unit in a first time duration(e.g., February of the current calendar year), and the historicgeographic region performance map corresponds to a second instance ofthe first recurrent time unit in a second time duration (e.g., Februaryof a previous calendar year). In an illustration of the first example, afirst geographic region performance map corresponding to January of 2015can be combined with a second geographic region performance mapcorresponding to January of 2016.

As shown in FIGS. 2-4, in a second example, geographic regionperformance maps can be combined to generate a geographic regionperformance summary map for a time unit within a time duration. In anillustration of the second example, the first geographic regionperformance map can be generated from an image captured on July 16 ofthe current growing season, the second geographic region performance mapcan be generated from an image captured on July 13 of the currentgrowing season, and the two geographic region performance maps can becombined to generate a geographic region performance summary map forJuly of the current growing season. The current geographic regionperformance summary map can be combined with a historic geographicregion performance summary map for July of the previous growing season.In a third example, representative geographic region performance summarymaps for a recurrent time unit (e.g., July) within a current growingseason can be combined with each available historic geographic regionperformance summary map for the recurrent time unit within previousgrowing seasons, thereby generating a multi-year performance map for therecurrent time unit.

In a second variation, geographic region performance maps correspondingto substantially the same time unit can be combined. For example,combining geographic region performance maps can include combining mapsmeasuring different performance characteristics of the same geographicregion at substantially the same time. In another example, geographicregion performance maps covering overlapping but different geographicregions at substantially the same time can be combined. However,combining as in S160 can include combining any suitable geographicregion performance maps covering any suitable geographic regions at anysuitable time units. Combining geographic region performance mapspreferably includes combining geographic region performance values ofthe geographic region performance maps. Additionally or alternatively,the combination of geographic region performance maps can include thecombination of vegetative performance values, combined geographic regionperformance values, normalized parameter values, and/or pixel values,but can include processing with any other suitable data.

In a third variation, geographic region performance maps correspondingto time units within the same time duration can be combined to generatea geographic region performance summary map for the time duration. Forexample, all geographic region performance maps for the geographicregion recorded during 2015 can be combined to create a geographicregion performance summary map for 2015.

In a first variation, combining the geographic region performance mapsincludes averaging the performance values (e.g., normalized vegetativeperformance values, geographic region performance values) of a firstgeographic performance map with geographic region performance values ofa second geographic performance map.

In a second variation, combining the geographic region performance mapsincludes identifying the mean performance value (e.g., normalizedvegetative performance value) for each geographic sub-region, across theset of geographic region performance maps.

In a third variation, combining the geographic region performance mapsincludes measuring the change in performance values (e.g., vegetativeperformance values, geographic performance values) of a first geographicregion performance map with respect to performance values of a secondgeographic region performance map.

In a third variation, combining the geographic region performance mapsincludes: weighting geographic region performance values and combiningthe weighted geographic region performance values. Geographic regionperformance values can be weighted on a map basis (e.g., wherein eachgeographic region performance value in the map takes on the map weight),on an individual basis (e.g., wherein different geographic regionperformance values within the same map have different weights), or onany other suitable basis. Geographic region performance valuesassociated with a higher confidence level of accuracy (e.g., of theunderlying data) can be weighted more heavily than geographic regionperformance values associated with a lower confidence level. Confidencelevel can be determined based on image quality, weather conditions, soilconditions, and/or any other suitable characteristic. For example, if afirst geographic region performance map corresponds to a time unit atwhich abnormal weather conditions were present, then geographic regionperformance values of the first geographic region performance map can beweighted less relative to geographic region performance mapscorresponding to time units associated with normal weather conditions.Alternatively, geographic region performance values can be weightedbased on temporal criteria. For example, geographic region performancevalues associated with a time unit closer to a present time can beweighted more heavily than geographic region performance valuesassociated with a time unit further in the past. However, the weightingscan be determined in any suitable manner and/or upon any suitablecriteria.

However, the geographic region performance maps can be otherwisecombined.

In a second variation, generating the geographic region performancesummary map for a geographic region for a recurrent time unit includes:identifying images of the geographic region recorded during therecurrent time unit (e.g., across multiple time durations); determiningthe vegetative performance value for each geographic sub-regionrepresented by an image element of the identified images; combining thevegetative performance value for each geographic sub-region across themultiple time durations; and normalizing the combined vegetativeperformance values across the set of geographic sub-regions. However,the geographic region performance summary map can be otherwisegenerated.

2.4 Generating a Crop Input Prescription.

As shown in FIGS. 4 and 5, the method 100 can additionally oralternatively include generating a crop input prescription S180, whichfunctions to enable variable rate crop treatment and management. Thecrop input prescription (generating a crop treatment prescription) canbe based on any number of geographic region performance summary mapsgenerated as in S160. Additionally or alternatively, generating a cropinput prescription S180 can be based on the individual geographic regionperformance maps generated as in S120, vegetative performance values ofthe geographic region, soil data (e.g., soil texture, soil hydraulicproperties, soil organic matter, etc.), weather data (e.g., dailytemperature, precipitation, radiation, etc.), user-inputted information,and/or crop management data (e.g., user-inputted data, historic seedingprescriptions, etc.). However, any suitable type of information can beused in generating the crop input prescription S180. The crop inputprescription can enable a crop type input (e.g., seeding, fertilizer,fungicide, etc.) to be variably applied with respect to time andlocation, but the prescription can enable the input to be variablyapplied with respect to any other suitable criteria. The crop inputprescription can vary aspects of crop input application based onvariance in geographic region performance indicated by geographic regionperformance summary maps, but the prescription can additionally oralternatively be varied with respect to variance in any other suitablecharacteristic associated with the geographic region. The application ofthe crop input is preferably capable of being varied at least at thegranularity level of the land dimensions corresponding to an imageelement of the image. For example, if a geographic region corresponds to5 square meters of a field, and an image element corresponds to 0.1square meters of the field, the crop input prescription can preferablyvary application of the crop input at the resolution of at least 0.1square meters. However, the crop input prescription can vary crop inputapplication at any suitable granularity level (e.g., with respect todistance, geographic region, geographic sub-region, image elements,etc.). Generation of a crop input prescription S180 can additionally oralternatively be based on user-selected preferences (e.g., types of cropinput, supply of crop input, preferred range of amount of crop inputapplication, preferred times of crop input application, etc.). Forexample, a user can input an amount and type of seeding supplyavailable, and a crop input prescription can be generated in accordancewith the user-inputted limitations. Alternatively, a crop inputprescription can be generated independent of user input. Types of cropinput prescriptions can include seeding prescriptions, fertilizerprescriptions, and/or fungicide prescriptions, but can typify anysuitable type of crop input prescription. Generating a crop inputprescription can include communication with components (e.g., seedingmachinery) to apply the crop input prescription. Additionally oralternatively, the crop input prescription can be sent to a user at auser device. However, the crop input prescription can includecommunication to any suitable entity in any suitable fashion.

In a first variation, generating a crop input prescription S180 includesgenerating a seeding prescription. The seeding prescription can includea variable seeding rate based on the variable performance of geographicsub-regions as indicated by, for example, a geographic regionperformance summary map. For example, a certain type of seed and/orseeding rate can be prescribed for higher performing geographicsub-regions, and a different type of seed and/or seeding rate can beprescribed for lower performing geographic sub-regions, in order toaccount for variations in soil productivity. Seeding rate can beformulated in units of seeds/acre, seed cost/acre, breakevenbushes/acre, and/or any other suitable units of measurement.Additionally or alternatively, the seeding prescription includes avariable seed type based on crop type corresponding to a geographicsub-region, performance of the sub-region, and/or any other suitablecriteria. However, the seeding prescription can prescribe seeding inputwith respect to any suitable criteria.

In a second variation, generating a crop input prescription includesgenerating a fertilizer prescription. The fertilizer prescription caninclude a nitrogen prescription, but can additionally or alternativelyinclude a phosphorous prescription, a potassium prescription, and/or anyother suitable type of fertilizer prescriptions.

2.6 Notifying a User.

As shown in FIGS. 4 and 5, the method 100 can include notifying a userS190, which functions to inform a user of a crop input prescriptionand/or performance indicators of the geographic region. Notifying a userS190 can be in response to generating the geographic region performancesummary map as in S160, and/or in response to generating the crop inputprescription as in S180. However, notifying the user S190 can beperformed prior to or after any suitable part of method 100. Notifyingthe user S190 can include presenting the user with the geographic regionperformance summary map, the crop input prescription, individualgeographic region performance maps, geographic region performancevalues, vegetative performance values, and/or any other suitable type ofdata. The user is preferably notified at a user device (e.g., mobiledevice, laptop, tablet, etc.) of the user, but can be notified throughany other means. In a specific example, the notification can includetreatment mechanism control instructions to achieve the crop inputprescription. The notification can be sent to the treatment mechanism,wherein the treatment mechanism executes or is otherwise controlledbased on the control instructions. The content of the notification ispreferably tailored to preferences selected by the user. Alternatively,the notification content can be determined irrespective of any userinput, but can otherwise be determined. The notification is preferablycommunicated to the user device by a remote server. However, anysuitable entity can notify the user at any suitable device or throughany suitable means.

2.6 Iterative Method Performance.

Parts of the method 100 can additionally be repeated for each of aplurality of images of substantially the same geographic region, wherethe plurality of images are recorded at different recurrent time unitstime duration. In one example, this can include repeating the method foreach of a plurality of images of the geographic region, where each imageof the plurality is taken at a different time within a growing season.

In a first variation, repeating the method for images of the geographicregion recorded over the time duration can function to provide a measureof the region's performance pattern over the time duration (e.g.,region's performance at different time units within the time duration).For example, this can provide a measure of how well the soil performs inMay versus its performance in June. This information can be used toprescribe treatments to accommodate for the anticipated performancechanges (e.g., plan to rent nitrogen application systems to augment theupcoming performance decrease next month), plan crop treatment schedules(e.g., planting, harvesting, fertilizing schedules, etc.), or be used inany other suitable manner.

In a second variation, repeating parts of the method for images of thegeographic region recorded over the time duration can additionallyfunction to provide additional input into the region's overallperformance. For example, a high performance geographic region canperform consistently well over the growing season (e.g., consistentlyhave vegetative performance values in the top percentile), whereas a lowperformance geographic region can perform well early in the growingseason, but perform poorly toward the end of the growing season. In thisvariation, the plurality of normalized values for each image elementover time can be averaged into a summary normalized value or otherwiseprocessed to provide a time-independent performance summary map.

Parts of the method can additionally be repeated for each of a pluralityof images of the same geographic region, where each of the plurality ofimages is recorded at substantially the same recurrent time unit acrossdifferent time durations. In one example, this can include repeating themethod for each of a plurality of images of the geographic region, whereeach image of the plurality is taken during a different time duration atsubstantially the same time unit within the respective time duration. Ina specific example, each image can be taken during the first weekdifferent growing seasons. In a second specific example, the pluralityof images includes a first image taken during May of a first year, asecond image taken during May of a second year, and a third image takenduring May of a third year. Repeating the method for images of thegeographic region recorded at substantially the same time unit acrossmultiple time durations can function to normalize the effects ofvariable factors that influence yield (e.g., differences in weather,crop type, insect and disease pressure, agronomic practices, etc.) onthe performance map. Normalized values derived from images recordedduring different time durations within a threshold time variation of agiven time unit are preferably aggregated and processed to generate theperformance summary value for each image element. For example, thenormalized pixel values from images collected within a week of May 3 foreach year can be aggregated and processed into the performance summaryvalue for each pixel. The performance summary value can be the averageof the normalized values across the different time durations for theimage element, the mean normalized value, or be any other suitableperformance summary value. However, the performance summary map can beotherwise generated.

3. EXAMPLES

In a specific example, as shown in FIGS. 2A and 2B, the method includesreceiving multiple sets of images (e.g., where each set of imagescorresponds to images recorded in a given year). Each image set can beassociated with a different time duration (e.g., a given year, a givengrowing season, etc.) and can include multiple subsets of images (e.g.,images of multiple geographic areas or tiles, multiple images of acommon geographic region where each image is recorded at approximatelythe same time unit in the year). Each subset of images can be capturedat a different recurrent time unit (e.g., January 5) within a given timeduration (e.g., captured at different time units within a given year).Each image within each image subset can be captured within a thresholdtime of the remainder of the subset, and each is representative of adifferent geographic region. Each image can be processed by determininga real-world geographic identifier for the geographic region captured bythe image, identifying the pixels forming the image, and determining thevegetative performance value for each pixel of the image. The vegetativeperformance values of each pixel can be normalized by grouping pixelscorresponding to the same type of crop (e.g., by overlaying secondarycrop information over the image based on the geographic identifier, suchas a cropland data layer), determining the percentile value of thevegetative performance value for each pixel based on the vegetativeperformance values of a representative pixel population, and using thepercentile value as the normalized value for the pixel. Therepresentative pixel population preferably includes pixels from the sameimage that correspond to the same crop as the pixel to be normalized,but can alternatively or additionally include pixels from the same imagesubset (e.g., where the images within the subset are stitched togetheror otherwise associated), pixels sharing the same crop and same timeunit within the same time duration, or include any other suitablepopulation of pixels. A geographic region performance map (e.g., virtualmap or model of the geographic region) can be generated based on thenormalized values. The geographic region performance map can be combinedwith a historic geographic region performance map corresponding to thesame geographic region, thereby producing a geographic regionperformance summary map. In this example, the method 100 canadditionally include summarizing the pixel value of images within arange of time units for each image set. In an illustration, all valuescorresponding to a predetermined growth stage (e.g., from a single imageset or from multiple image sets) can be binned into a single geographicregion performance map of a geographic region. In a first specificillustration, all measurements associated with a first geographic regionfrom a first month for a first year can be binned together into aperformance summary map. In a second specific illustration, allmeasurements associated with a second geographic region from a firstmonth for a plurality of years can be binned together into a performancesummary map.

An alternative embodiment preferably implements the above methods in acomputer-readable medium storing computer-readable instructions. Theinstructions are preferably executed by computer-executable componentspreferably integrated with a baseline performance determination system.The baseline performance determination system can include a vegetativeperformance value extraction system, parameter value normalizationsystem, and a mapping system configured to map the normalized parametervalue to the source geographic location. The computer-readable mediummay be stored on any suitable computer readable media such as RAMs,ROMs, flash memory, EEPROMs, optical devices (CD or DVD), hard drives,floppy drives, or any suitable device. The computer-executable componentis preferably a processor but the instructions may alternatively oradditionally be executed by any suitable dedicated hardware device.

Although omitted for conciseness, the preferred embodiments includeevery combination and permutation of the various system components andthe various method processes.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. A method for measuring performance of a geographic region,the geographic region including a set of geographic sub-regions, themethod comprising: a) receiving a first image of a physical geographicregion, the first image recorded during a recurrent time unit within afirst time duration, the first image comprising an image element set; b)for each image element of the image element set: mapping the imageelement to a virtual representation of a geographic sub-region withinthe geographic region, determining a vegetative performance value forthe virtual representation of the geographic sub-region based on aspectral signal extracted from the respective image element, andassociating the virtual representation of the geographic sub-region witha crop type from a set of crop types; c) identifying a first crop setcomprising virtual representations of geographic sub-regions associatedwith a first crop type of the set of crop types; and d) determining afirst normalized vegetative performance value set, comprisingdetermining a normalized vegetative performance value for each virtualrepresentation of a geographic sub-region within the first crop set.